2.2.3.1 Districtwise:

Analysis of districtwise revenue then can be done to get an overall picture of the performances. Table 2.4¹⁶ and Figures 2.4¹⁷,

2.5¹⁸ render necessary insights.

Table 2.4: District wise Revenue etc. data of the State

District	Rev1	Doc1	Rev2	Doc2	Rev3	Doc3	RvGr1	DocGr1	RvGr2	DocGr2	rvshr	docshr
North 24 Pgs	9099109502	166273	10359659124	169756	8620868655	139679	13.8	2.09	-16.8	-17.7	17.11	10.53
South 24 Pgs	9796915300	168612	9582419081	166082	8162951031	132864	-2.2	-1.50	-14.8	-20.0	16.79	10.35
Kolkata	8638565070	39521	8750639700	37972	7656368001	30458	1.3	-3.92	-12.5	-19.8	15.26	2.39
Howrah	3314022644	65140	3577384405	65208	3022909976	56540	8.0	0.10	-15.5	-13.3	6.04	4.14
Hooghly	2891822677	90529	3155666187	91062	2933136319	80928	9.1	0.59	-7.0	-11.1	5.47	5.81
Murshidabad	2473640384	202548	2785823500	190901	2878197961	183612	12.6	-5.75	3.3	-3.8	4.96	12.78
Nadia	2139596813	110470	2367387903	112439	2299628451	102809	10.6	1.78	-2.9	-8.6	4.15	7.21
Purba Medinipur	2104400057	121110	2471051527	127736	2225178928	108886	17.4	5.47	-9.9	-14.8	4.14	7.92
Paschim Bardhaman	1721325216	29580	1912470170	29555	1782985575	24242	11.1	-0.08	-6.8	-18.0	3.30	1.85
Purba Bardhaman	1635639149	79265	1818073330	76687	1792965203	69686	11.2	-3.25	-1.4	-9.1	3.20	5.00
Paschim Medinipur	1575007081	84979	1734175528	86585	1797313742	79014	10.1	1.89	3.6	-8.7	3.11	5.55
Malda	1338780400	90081	1502651419	89951	1474247771	84581	12.2	-0.14	-1.9	-6.0	2.63	5.86
Darjeeling	1408965395	18643	1570927838	17692	1327486323	14270	11.5	-5.10	-15.5	-19.3	2.63	1.12
Jalpaiguri	1293957702	24310	1372519715	24501	1216647438	20613	6.1	0.79	-11.4	-15.9	2.37	1.54
Birbhum	1088086163	86694	1293050290	81410	1271861536	73776	18.8	-6.10	-1.6	-9.4	2.23	5.36
Coochbehar	753572656	38726	893092112	40180	818863719	34706	18.5	3.75	-8.3	-13.6	1.50	2.52
Uttar Dinajpur	716261832	50129	822597465	52777	850584039	42475	14.8	5.28	3.4	-19.5	1.46	3.22
Bankura	730258664	32462	799609746	33200	700004964	32394	9.5	2.27	-12.5	-2.4	1.36	2.17
Purulia	429052948	20892	460253298	20803	445389144	18625	7.3	-0.43	-3.2	-10.5	0.81	1.34
Dakshin Dinajpur	356568290	35097	438897467	35549	428151202	33207	23.1	1.29	-2.4	-6.6	0.75	2.30
Alipurduar	180290305	7875	238320997	8177	224431851	7256	32.2	3.83	-5.8	-11.3	0.39	0.52
Jhargram	127899241	6410	173119562	6853	178426179	6675	35.4	6.91	3.1	-2.6	0.29	0.44
Kalimpong	25954435	1293	19540939	1389	19265768	1403	-24.7	7.42	-1.4	1.0	0.04	0.09
a Rev1 = Revenue in F.Y. 2018-19
b Rev2 = Revenue in F.Y. 2019-20
c Rev3 = Revenue in F.Y. 2020-21
d Doc1 = Number of Documents registered in 2018-19
e Doc2 = Number of Documents registered in 2019-20
f Doc3 = Number of Documents registered in 2020-21
g RvGr1 = Revenue Growth in 2019-20 w.r.t 2018-19
h RvGr2 = Revenue Growth in 2020-21 w.r.t 2019-20
i DocGr1 = Document Growth in 2019-20 w.r.t 2018-19
j DocGr2 = Document Growth in 2020-21 w.r.t 2019-20
k rvshr = Percentage of Revenue contributed towards the state total- averaged over 3 F.Y.s- 2018-19 to 2020-21
l docshr = Percentage of the Number of documents registered to the state’s total number: averaged over 3 F.Y.s- 2018-19 to 2020-21

These show

• quite expectedly (Please refer to discussions in Sec 2.2.2)- North 24 Parganas, South 24 Parganas, Kolkata (or RA_kolkata), Howrah, Hooghly are top five revenue contributors. Together they put up about 60% of the total revenue ( Graph 1 of Fig 2.4);

• while, Murshidabad,North 24 Parganas, South 24 Parganas, Purba Medinipur, Nadia, Malda are the top contributors towards total number of documents registered annually.Together they share around 55% of the total numbers (Graph 2 of Fig 2.4);

• that In 2019-20 most of the districts fared well and only four districts fell below the state growth of 7.91% (Graph 1 of Fig 2.5);

• while in 2020-21 only four districts, namely Murshidabad, Paschim Medinipur, Jhargram, Uttar Dinajpur-together which contribute only 10% of the total revenue (Uttar Dinajpur’s share is 1.51% and Jhargram’s 0.3%)- shown positive growth, and excepting Hooghly, all the top five revenue earning districts fell below the state growth of -10.26% (Graph2 of Fig 2.5).

2.2.3.2 Officewise:

Likewise, similar exercises can be taken up for offices.

• Table 2.5 has been prepared by picking up State’s top revenue earning offices and sorting them in descending order of annual revenue they earned.

• For sorting F.Y 2019-20 has been considered as most of the top offices fared well in that year.

• Also revenue, number of documents for last three financial years, revenue,document-growth for the corresponding years have been taken into account.

• Figures 2.6 and 2.7 show the respective revenue collected at these offices during 2018-19,2019-20 and in 2020-21.

Table 2.5: High yielding Revenue Offices

Distr	Offc	dist	rev1819	rev1920	rev2021	revgr1	docgr1	revgr2	docgr2
Kolkata	ARA-IV	1	3090473570	2822566440	1877468684	-8.67	-6.37	-33.5	-36.71
North 24 Pgs	ADSR Rajarhat	17	2419612699	2661522967	1840533296	10.00	5.57	-30.9	-30.55
Kolkata	ARA-III	1	710176656	2447448710	2279198517	244.63	-15.56	-6.9	-8.43
Kolkata	ARA-II	1	1680205201	1748495662	1268518799	4.06	91.22	-27.4	-29.12
Kolkata	ARA-I	1	3157709643	1732128888	2231182001	-45.15	-32.56	28.8	6.02
North 24 Pgs	ADSR DumDum	7	1146724015	1501952464	1167302768	30.98	8.59	-22.3	-28.25
South 24 Pgs	ADSR Sealdah	3	994012034	1185823745	854304168	19.30	-9.91	-28.0	-26.59
South 24 Pgs	ADSR Alipore	5	1442220130	1181069168	698916033	-18.11	-17.59	-40.8	-54.14
Howrah	ADSR Howrah	5	1101457326	1154831594	917804673	4.85	-5.52	-20.5	-16.93
South 24 Pgs	DSR-II South 24-Parganas	5	1040724537	982038481	996999578	-5.64	-6.38	1.5	-23.36
Jalpaiguri	ADSR Bhaktinagar	574	827280498	898590359	679124248	8.62	3.43	-24.4	-32.49
North 24 Pgs	ADSR Bidhannagar	9	616492020	875302782	641597404	41.98	32.76	-26.7	-27.08
South 24 Pgs	ADSR Behala	12	792624591	857197043	846843584	8.15	4.70	-1.2	-10.07
Darjeeling	ADSR Bagdogra	555	744236136	847811045	584190403	13.92	-0.74	-31.1	-37.72
South 24 Pgs	DSR-III South 24-Parganas	5	880674486	789072212	738717948	-10.40	-7.14	-6.4	1.19
Howrah	DSR-II Howrah	5	675866377	757032189	741429243	12.01	1.58	-2.1	10.92
North 24 Pgs	ADSR Sodepur	19	641386488	711409736	740134689	10.92	2.68	4.0	-12.06
Paschim Bardhaman	ADSR Durgapur	185	586530009	709141931	622286259	20.90	7.00	-12.2	-12.22
Paschim Bardhaman	ADSR Asansol	225	606695304	683903972	622286259	12.73	-4.65	-9.0	-28.45
Murshidabad	ADSR Sadar Murshidabad	200	548744131	643593931	651747841	17.28	-1.54	1.3	0.08
South 24 Pgs	ADSR Garia	17	512178418	637753266	532037798	24.52	8.66	-16.6	-15.71
Howrah	DSR-I Howrah	5	425018764	610746547	451179167	43.70	32.02	-26.1	-25.77
North 24 Pgs	ADSR Barasat	25	623532245	608010542	545666234	-2.49	-10.29	-10.2	-19.86
South 24 Pgs	DSR-IV South 24-Parganas	5	690377647	600550294	718417794	-13.01	-8.66	19.6	2.26
North 24 Pgs	DSR-I North 24-Parganas	25	389707012	555215084	491030399	42.47	28.36	-11.6	-14.82
North 24 Pgs	DSR-III North 24-Parganas	25	458685293	531444626	539227113	15.86	16.07	1.5	-15.71
Purba Bardhaman	ADSR Sadar Purba Burdwan	103	475166722	528098384	509331744	11.14	-5.01	-3.5	-19.38
North 24 Pgs	ADSR Naihati	47	513558441	515645553	533810605	0.41	-11.30	3.5	-5.72
South 24 Pgs	DSR-I South 24-Parganas	5	797249737	507101526	283323329	-36.39	-17.70	-44.1	-39.87
a dist = Distance from State caital/State HQ,in Km
b rev1819 = Revenue in 2018-19
c rev1920 = Revenue in 2019-20
d rev2021 = Revenue in 2020-21
e revgr1 = Revenue growth in 2019-20 w.r.t 2018-19
f revgr2 = Revenue growth in 2020-21 w.r.t 2019-20
g docgr1 = Document growth in 2019-20 w.r.t 2018-19
h docgr2 = Document growth in 2020-21 w.r.t 2019-20

These show

• that the 100 Cr+ (or A+++ category as discussed earlier) offices are situated within 15-20 km of the state headquarters and include RA offices and several offices of Alipore, and ADSRs Rajarhat, Sealdah, Dumdum (ADSR Cossipore Dumdum - to be more specific);

• that the rest offices (mostly A++ categories) fall mostly in Kolkata’s suburb districts such as North 24 Parganas, South 24 Parganas, Howrah ; as well as in the prominent growth areas like Asansol-Durgapur industrial zone (ADSR Asansol,ADSR Durgapur) and Siliguri(ADSR Bhaktinagar in Jalpaiguri district and ADSR Bagdogra, ADSR Siliguri in Darjeeling district) and in some District head quarters over important communication networks (ADSR Burdwan Sadar, ADSR Murshidabad Sadar).

Further revenue-growths of these offices can be studied by plotting them in Figure 2.8 (contains two plots,the second one been adjusted by keeping out ARA-III-whose high number dwarfed the others) for 2018-19 and in Figure 2.9 - for 2020-21 respectively.

Findings are as follows-

In 2018-19. (Please refer Fig 2.8)

• ARA-III - even with negative document growth (-15.56 %) showed revenue growth of a stellar 244.63%;
• ARA-II’s document growth was 91% and revenue grew at 4.06 %;
• ARA-IV’s document growth was -6.37% and revenue growth -8.67%;
• ARA-I’s document growth was -32.56% and revenue growth -45.15%; It is worth-mentioning that during this period - jurisdiction of individual ARA offices were lifted and during first phases of implementation system by itself would pick up the offices - which however was sorted out in very short time.

Nevertheless, few notable things happened around this-

• It led to the exodus of revenue from ARA offices towards suburb offices.¹⁹
• Offices like ADSR Dumdum (ADSR Cossipore Dumdum, more specifically), ADSR Bidhannagar, ADSR Sealdah, ADSR Garia, DSR-I & DSR-II Howrah, ADSR Sodepur seems to have benefited most in this situation;
• Among ARA offices - ARA III seems to have registered more documents involving property transfer instead of documents involving other than immovable property that it would do earlier.
• A large chunk of latter kind of documents seems to have been registered at ARA-II;
• However, offices at Alipore could not reap the benefits.

Hence it can be easily inferred that ARA offices, offices at Alipore, ADSR offices Rajarhat, Sealdah, Dumdum, Behala, Garia, Barasat, ²⁰ Sodepur, Naihati and offices at Howrah Sadar - share among themselves the revenue of greater Kolkata urban area. These offices have a tendency to grow or shrink at the expense of the others having jurisdiction common in themselves.²¹.Generally, far the office is from Kolkata - sharing of revenue with ARA offices decreases.²².

Apart from these offices, ADSRs Durgapur, Berhampore, Asansol, Bagdogra, Burdwan, Bhaktinagar showed positive revenue growth.

In 2020-21 (Please refer Fig 2.9)

• Pandemic took its toll and most of these offices showed negative revenue growth.
• Only ARA- I showed significant positive growth. Decreased revenue of ARA-IV seems to have been distributed among ARA-I and ARA-III.
• Meanwhile jurisdiction among the DSRs in the districts has been lifted in this financial year. It seems to have benefited DSR-IV South 24 Parganas during this period.

It is found from the above analysis that revenue of top offices in Kolkata and its immediate suburbs are more or less distributed among themselves. With Covid looming above and demand shrinking, it is more or less becoming saturated. ²³ Hence it is high time that DoRSR looked beyond these offices and

• Starts thorough analysis of the next important levels, one by one, and examine regularly the registration practices prevalent in these offices,

• analyzes how they prepare their MV and other related database,

• and guiding them

  • how efficiently those can be handled by employing lesser subjectivity;
  • and the growth centers be identified properly and revenue from them can be tapered of.

2.3.1.1 District and PS wise summarsing of different mouzatypes:

All the mouzas have been grouped under respective Districts, PSs and numbers of different types, namely ‘Rural’,‘Developing’etc (Please refer to the discussion in Sec 2) and have been summarised in table 2.6 - specially the proporionate number of ’Developing’ mouzas has been taken into consideration. Any high number, specially in not so developed zones may be picked up.Table 2.6 tesifies presence of such and can be supported by Fig 2.10.

Table 2.6: Analysis of District & PS wise Mouza Types

Dist	PS	TotMouz	Rrl	Dev	Muni	Muni1	Muni2	Muni3	Dev_prop
South 24 Parganas	Kolkata Leather Camp	31	0	31	0	0	0	0	100
Purba Bardhaman	Ketugram	122	7	114	1	0	1	0	93
Howrah	Sankrail	40	3	35	2	0	2	0	88
Hooghly	Magra	52	0	45	7	1	6	0	87
Darjeeling	Matigara	57	7	49	1	1	0	0	86
Nadia	Kaliganj	127	24	103	0	0	0	0	81
Nadia	Nakashipara	109	23	86	0	0	0	0	79
Hooghly	Polba	97	21	76	0	0	0	0	78
North 24 Parganas	Rajarhat	158	28	109	21	14	7	0	69
North 24 Parganas	Amdanga	81	27	54	0	0	0	0	67
Murshidabad	Kandi	101	31	59	11	5	3	3	58
Purba Medinipur	Nandakumar	101	44	57	0	0	0	0	56
Howrah	DOMJUR	55	25	30	0	0	0	0	55
Darjeeling	Kurseong	137	57	74	6	1	5	0	54
North 24 Parganas	Titagarh	15	2	8	5	5	0	0	53
Coochbehar	Haldibari	62	27	32	3	3	0	0	52
South 24 Parganas	Budge Budge	34	4	17	13	7	6	0	50
Dakshin Dinajpur	Tapan	279	141	138	0	0	0	0	49
Hooghly	Balagarh	136	69	67	0	0	0	0	49
Howrah	Panchla	33	17	16	0	0	0	0	48
a Dist = District Name
b PS = Police Station Name
c TotMouz = Total Number Mouzas in that PS
d Rrl = Total Number of Rural Mouzas in that PS
e Dev = Total Number of Developing Mouzas in that PS
f Muni = Total Number of Municipality Mouzas in that PS
g Muni1 = Total Number of Municipality Type 1 Mouzas in that PS
h Muni2 = Total Number of Municipality Type 2 Mouzas in that PS
i Muni3 = Total Number of Municipality Type 2 Mouzas in that PS
j Dev_prop = Proprtion of Developing mouzas to Total Number of Mouzas in that PS
k Rows have been created in the descending order of Dev_proportion in the state and only the top 30 have been shown here.

From Table above, it is found that there are many police stations in the state with high ‘Developing’ to ‘Total’ mouza ratio ( here, Developing_Proportion ).

• However, keeping in view of the level of development for PSs like ‘Kolkata Leather Complex’,‘Sankrail’,‘Rajarhat’,‘Domjur’ presence of such high numbers can be explained.
• But, for PSs like
  • Ketugram(Number of Developing Mouzas 114 out of total 122 ),
  • Magra(45 out of 52) ,
  • Kaliganj(103 out of 127),
  • Nakashipara(86 out of 109),
  • Kandi(59 out of total 158 of which 11 are Municipality mouzas),
  • Nandakumar(57 out of 101 ),
  • Haldibari(32 out of total 62 of which 3 are Municipality mouzas),
  • Balagarah(67 out of 136),
  • Tapan(138 of 279),
  • Swarupnagar(30 out of 66), it seems unlikely²⁴ and warrants for further study of the Conversion Ratios in general and that of practices around their usages during registration.

2.3.1.2 District and Officewise summarising of Ratios:

Generally speaking,‘Conversion Ratio’ is the list of factors assigned for all landgroups and mouza-types of a ADSR office. Usually all the land-types procured from ‘Land Department’ are put into different categories or land-groups by ADSRs based on the similarity of usages. Each land-group has its own set of multiplicative factors. ²⁵.Table 2.7 is an example of the format as to how conversion ratios of ADSR offices are stored.

Table 2.7: Example of a Conversion Factor Table:ADSR BANKURA

Dtcd	Dt	Rcd	offnm	uscd	clcd	class	rrl	dev	m1	m2	m3	crp
01	Bankura	02	A.D.S.R. BANKURA	001	01	Shali	1.0000	1.0000	2.0000	2.0000	2.0000	NULL
01	Bankura	02	A.D.S.R. BANKURA	002	03	Suna	2.0000	1.0000	2.0000	2.0000	2.0000	NULL
01	Bankura	02	A.D.S.R. BANKURA	003	04	Doe	3.0000	1.0000	2.0000	2.0000	2.0000	NULL
01	Bankura	02	A.D.S.R. BANKURA	004	01	Baide	1.0000	1.0000	2.0000	2.0000	2.0000	NULL
01	Bankura	02	A.D.S.R. BANKURA	005	02	Kanali	1.5000	1.0000	2.0000	2.0000	2.0000	NULL
01	Bankura	02	A.D.S.R. BANKURA	008	02	Sole	1.5000	1.0000	2.0000	2.0000	2.0000	NULL
01	Bankura	02	A.D.S.R. BANKURA	457	23	Danga Land	6.0000	3.0000	2.0000	2.0000	2.0000	0.0000
01	Bankura	02	A.D.S.R. BANKURA	013	08	Bastu	12.0000	4.0000	2.0000	2.0000	2.0000	NULL
01	Bankura	02	A.D.S.R. BANKURA	456	07	Vitti/Vitta	12.0000	4.0000	2.0000	2.0000	2.0000	NULL
01	Bankura	02	A.D.S.R. BANKURA	018	10	Khamar	10.0000	3.5000	2.0000	2.0000	2.0000	0.0000
a Dtcd = District Code
b Dt= District
c Rcd= RO Code
d offnm = Office Name
e uscd = Land Use Code - prepared at DoRSR based on the grouping proposed by ADSRs
f clcd = Land Class Code - maintained by Land department
g class = Land Class supplied primarily by Land Bepartment. On few occasions ADSRs have also introduced few classes.
h rrl = Conversion Ratio for Rural Mouzas
i dev = Conversion Ratio for Developing Mouzas
j m1 = Conversion Ratio for Municipality1 Mouzas
k m2 = Conversion Ratio for Municipality2 Mouzas
l m3 = Conversion Ratio for Municipality3 Mouzas
m crp = Conversion Ratio for orporation Mouzas : Applicable for Corporation area of KMC, HMC, BMC etc.
n For discussion on grouping of mouzas, please refer to Sec 2: Approach

Market value of a property is determined by multiplying the basevalue of that particular property and the conversion factor assigned for that particular landgroup(of that very mouza-type) to which its landtype belongs. ROs determine the proposed use of that particular property by employing their field knowledge and use the respective conversion ratio to reach at the market value of that property- which leaves an ample scope of subjectivity.

So a study of the officewise number of landgroups needs to be undertaken and it is to be reconciled with the registration data of the offices in regular interval to find out whether the landgroups (and in turn conversion ratios) are actually being utilised properly.

Table 2.8, Figure 2.11 and Figure 2.12) have been prepared for this purpose.

• Table Table 2.8 has two separate tables T1 and T2.

  • T1 shows districtwise list of median²⁶number of landgroups.

  • T2 lists the number of landgroups and the number of offices having that particular number of landgroups (or set of conversion ratios) and what proportion is that number to the toatal number of offices of State.

• Figure 2.11 summarises the number of offices with corresponding number of landgroups.

• Figure 2.12 plots all the offices alongwith their corresponding number of landgroups.With the help of boxplots, it also plots the distribution of the number of landgroups within a district across its offices.

From these, it is found that

• ADSR offices do have a lot of variations in the number of landgroups- starting from 2 to 62. Figure 2.12).
• Median number of landgroups for all offices of state is 13.
• There are 49 offices (21.74%) with landgroups more than 20. (Table 2.8)
• There are 74 offices (32.87%) with landgroups less than or equal to 10,
• And, 109 offices (48.46%) have landgroups 11-20.
• 20 offices for each have 11 and 15 landgroups.(Fig 2.11).
• In general,offices of the districts Purulia (24), Murshidabad (21), Purba Bardhaman (18) , Kalimpong(17),Paschim Medinipur(17) and Nadia(16) have generally much higher number of land groups.

Table 2.8 below captures the a picture of diffent number of landgroups,number of offices and a summary of district wise distribution of the number of landgroups.

It has two parts.

• In one Part 1 it shows district wise median number of landgroups.
• And Part 2 summarises different number of landgroups along with respective number of offices having it.

Table 2.8: Landgroups summary

Part 1 :District wise Median Number of Landgroups	Part 2:Number of Land groups and Number of offices
District Mdlg Purulia 24 Murshidabad 21 Purba Bardhaman 18 Kalimpong 17 Paschim Medinipur 17 Nadia 16 Bankura 15 Darjeeling 15 Hooghly 15 Paschim Bardhaman 15 Birbhum 14 Jhargram 14 Malda 14 Purba Medinipur 13 Jalpaiguri 12 North 24 Parganas 12 Alipurduar 10 Howrah 10 South 24 Parganas 10 Coochbehar 8 Uttar Dinajpur 8 Dakshin Dinajpur 7 Kolkata 2	Num_of_landgroups Num_of_offc proportion 11 20 8.89 15 20 8.89 10 19 8.44 8 16 7.11 9 16 7.11 7 13 5.78 16 13 5.78 13 10 4.44 12 9 4.00 14 8 3.56 17 8 3.56 18 8 3.56 20 7 3.11 21 7 3.11 19 6 2.67 6 5 2.22 23 5 2.22 25 4 1.78 26 3 1.33 5 2 0.89 22 2 0.89 24 2 0.89 27 2 0.89 28 2 0.89 29 2 0.89 34 2 0.89 36 2 0.89 2 1 0.44 3 1 0.44 4 1 0.44 30 1 0.44 31 1 0.44 32 1 0.44 33 1 0.44 35 1 0.44 46 1 0.44 58 1 0.44 60 1 0.44 62 1 0.44
1 Part 1:Mdlg = Median number of Landgroups of that District,rounded to nearest whole number
2 Part 2:Num_of_landgroups= Number of land groups
3 Part 2:Num_of_offc= Number of offices with that particular number of land groups
4 Part 2:proportion = Proportion of the Number of offices in that particular category to total number of offices
5 Explanation of Part 2:1st row has 11 in ‘Num_of_landgroups’ column and 20 in ‘Num_of_offc’ column, it means 20 offices have 11 landgroups.

Table 2.9 below shows the list of few offices having higher number of landgroups.

Table 2.9: Offices with most number of land groups: Top 10 of the State

District_code	Disrict	ro_code	office	num_of_landgrps
12	Murshidabad	15	A.D.S.R. JANGIPUR	62
03	Birbhum	03	A.D.S.R. BOLPUR	60
12	Murshidabad	26	A.D.S.R. SUTI	58
14	Purulia	04	A.D.S.R. JHALDA	46
10	Paschim Medinipur	06	A.D.S.R. BALICHAK	36
16	South 24 Parganas	11	A.D.S.R. BARUIPUR	36
02	Purba Bardhaman	19	A.D.S.R. RAINA	35
14	Purulia	02	A.D.S.R. PURULIA	34
23	Paschim Bardhaman	04	A.D.S.R. RANIGANJ	34
01	Bankura	02	A.D.S.R. BANKURA	33

Primary analyses of registration data (See Sec 2.3.2) of few select offices as well as the field experience tell that - whatever be the number of landgroups in their respective databases, ROs mostly use 6-7 landgroups or conversion ratios during market-value determination . And these revolve largely around variants of ‘agricultural’,‘waterbody’,‘residential’,‘commercial’ and in some cases ‘industrial’ groups of usages.

Hence, existence of higher number of landgroups eventually increases the chance of ambiguous land-groups’ presence in the database, which in turn enhances the chance of ambiguity around market value determination.

And keeping in mind the fact that registration generally covers only around 5% of the plots of any jurisdiction and generally occurs(often with repetition) around some well-known pocketed areas(See Fig 2.15); stress should be put in to encourage the ROs to prepare the database of basevalue of properties more carefully** by catching up the ground reality of location, level of development using plot maps, satellite imageries etc, so that reliance over conversion ratios can be minimised.**

In this exercise ROs can be helped by making them aware of the registration pattern of their offices and by helping them to identify growth zones of their jurisdictions.

2.3.2.1 Identification of Important/Potential growth zones:

It is known fact that registration of apartments play a crucial role towards revenue of the urbanised areas - so, for identification of important growth zones within the jurisdiction, relative share of land and apartment registration also needs to be looked into - starting from the jurisdiction level then gradually to PS level and to mouza and plot level needs to be analysed.

Table 2.10 has been created to show the share of land and apartment registration for whole of the jurisdiction.

Table 2.10: Contribution of land and apartment in registration: ADSR Raniganj

L/A	num_plts	prop_plts	prop_rv
A	2563	4.6	16
L	52894	95.4	84
1 L/A = Land or Apartment; L = Land and A = Apartment.
2 num_plts = Number of plots registered.
3 prop_plts = Proportion of plots involved.
4 prop_rv = Proportion of Revenue from respective categories

It is seen that land contributes 83.66% of revenue with 95.38% of plots registration and apartments contribute 16.34% of revenue involving 4.62% of plots registration.

Analysis of Table 2.11 and Table 2.12 in which PS wise and mouza wise data (for top 20 revenue earning mouzas) have been grouped and summarised respectively and Fig 2.15- which shows the distribution of market value across the plot numbers, will gradually show the areas where apartment registration is concentrated.

In Table 2.11 PS wise share of revenue and plots involved in registration have been summarised. Also alongside, proportionate share of rural, developing and municipalities of the respective PSs have been looked into.

Table 2.11: PS wise analysis of transaction: ADSR Raniganj

PS and Proportions			Revenue Share of Mouza Types			Revenue Share of Land /Apartment
PS	prp_rv	prp_plts	rrl_rev_pr	dev_rev_pr	muni_rev_pr	lnd_pr	apt_pr
Raniganj	39.1	27	13	0.0	87	64	36.41
Jamuria	29.9	38	39	1.4	60	100	0.30
Andal	23.7	23	97	3.1	0	92	8.32
Pandabeswar	7.4	12	100	0.0	0	99	0.83
1 prp_rv = Proportionate Revenue share;
2 prp_plts = Proportion of number of plots trnsferred;
3 rrl_rev_pr = Contribution towards revenue of this PS of Rural mouzas of this PS;
4 dev_rev_pr = Contribution towards revenue of this PS of Developing mouzas of this PS;
5 muni_rev_pr = Contribution towards revenue of this PS of Municipality mouzas of this PS;
6 lnd_pr = Revenue proportion from land registration;
7 apt_pr = Revenue proportion from land registration;

In Fig 2.13 PS wise proportion of revenue and transferred plots have been shown.

From Table 2.11 and from Fig 2.13 it is found that

• Raniganj contributes most to the revenue (39.06%);
  • of it ‘Municipality’ mouzas contribute 87% while ‘Rural’ mouzas contribute 13%.
  • Apartment registration contributes 36.41% of the revenue coming from Raniganj PS.
• Jamuria’s revenue share is 29.92%;
  • Respective share of Rural,Developing and Municipality mouzas are 39%,1.43% and 59.6% respectively;
• Andal’s revenue share is 23.66%;
  • Respective share of rural,developing mouzas are 96.9% and 3.14% respectively. There is municipalities in Andal PS.
  • Apartment registration contributes 8.32% of the revenue coming from Raniganj PS.
• Pandabeswar’s share of revenue is 7.36%;
  • All of it comes from rural mouzas as there are no developing and municipality mouzas in Pandabeswar PS.
• Jamuria registers most number of plots(37.63%), followed by Raniganj(27.31%),Andal(22.77%) and Pandabeswar(12.29%).
• Apartment registration in Jamuria and Pandebswar is not significant.

In mouza wise summary, mouzas with respective number of plots transferred during this period, their proportionate number to that of total, proportinate revenue, average area of land transfer²⁷,percentage of plots of that particular mouza registered for lands and for apartments, proportionate share of revenue from land and from apartment. Also it has been checked how important is the role of apartmenent registration for its jurisdiction. For this purpose, proportionate number of apartment transfers of each mouza to the total number for apartment registration of entire jurisdiction and the proportionate revenue from apartment to revenue from all apartment transfers have been checked out.(See Part IV of Tab 2.12).

In Table 2.12 top 20 mouzas which contribute most to revenue have been listed. Fig 2.14 shows these mouzas with their respective revenue and plots share.In it different mouza-types have been color-coded.

Table 2.12: Top Mouzas At a Glance: ADSR Raniganj

I:PS-Mouza				II:Share to Total				III:Distribution:Land-Apt						IV:Apt-Anls
PS	Mty	Mcd	Mouza	nPts	Pr_pts	Pr_rv	avAR	nldpts	nappts	nld_pr	nap_pr	rld_pr	rap_pr	Tnap_pr	Trap_pr
Raniganj	OM2	24	Raniganj Municipality	3189	5.8	14.8	2.7	2236	953	70	29.88	50	50.02	37.18	7.38
Raniganj	OM2	17	Searsole	2284	4.1	7.8	4.3	1413	871	62	38.13	50	50.25	33.98	3.91
Raniganj	OM2	18	Amrasata	1188	2.1	6.2	6.2	755	433	64	36.45	53	47.16	16.89	2.93
Andal	ROO	32	Khandra	2746	5.0	5.3	5.6	2739	7	100	0.25	100	0.48	0.27	0.03
Jamuria	OM1	37	Mandalpur	995	1.8	3.4	13.7	995	0	100	0.00	100	0.00	0.00	0.00
Jamuria	ROO	6	Churulia	1807	3.3	3.2	11.0	1806	1	100	0.06	100	0.00	0.04	0.00
Andal	ROO	18	Ukhra	895	1.6	3.0	4.4	889	6	99	0.67	99	0.82	0.23	0.02
Andal	ROO	52	Andal	1063	1.9	2.1	3.6	1063	0	100	0.00	100	0.00	0.00	0.00
Raniganj	RM2	13	Egara	2094	3.8	2.0	4.9	2094	0	100	0.00	100	0.00	0.00	0.00
Jamuria	OM1	35	Bijpur	1101	2.0	2.0	10.3	1101	0	100	0.00	100	0.00	0.00	0.00
Andal	ROO	41	Kajora	864	1.6	1.8	4.4	863	1	100	0.12	100	0.23	0.04	0.00
Andal	ROO	42	Bhadur	586	1.1	1.8	3.7	478	108	82	18.43	48	52.23	4.21	0.94
Pandabeswar	ROO	12	Jawalbhanga	819	1.5	1.8	16.0	819	0	100	0.00	100	0.00	0.00	0.00
Jamuria	OM1	21	Jamuria	764	1.4	1.7	7.6	764	0	100	0.00	100	0.00	0.00	0.00
Andal	ROO	51	Ramprasadpur	830	1.5	1.6	2.4	780	50	94	6.02	85	14.93	1.95	0.24
Jamuria	OM1	28	Nigha	739	1.3	1.6	5.5	738	1	100	0.14	98	2.25	0.04	0.04
Andal	ROO	50	Baska	780	1.4	1.6	3.2	690	90	88	11.54	56	44.41	3.51	0.69
Jamuria	OM1	19	Nandi	1030	1.9	1.5	12.7	1030	0	100	0.00	100	0.00	0.00	0.00
Andal	ROO	55	Dubchurieria	807	1.5	1.5	3.7	807	0	100	0.00	100	0.00	0.00	0.00
Jamuria	ROO	53	Tapsi	962	1.7	1.4	8.4	962	0	100	0.00	100	0.00	0.00	0.00
a It has been divided into 4 parts I:PS-Mouza = Information regarding Mouza,JL-No,respective PS, II:Share to Total= Contribution of the particular towards total number of plots registered and total revenue, III:Distribution:Land-Apt= Contribution of land and apartment towards plots registered and revenue of this mouza IV:Apt-Anls= Contribution of Apartments towards Total number of registration and revenue
b PS= PS-Name,Mcd = Mouza-code geneally JL No,Mty= Mouza-Type,,Mouza= Mouza-Name;
c npts= Total number plots registered in this mouza,Pr-pts= Proportionate number to total,Pr_rev=Proportionate revenue, avAR = Average area of land transferred in decimal, nldpts= Number of land plots registered, nappts= that of apartment, nld_pr = Land plot proportion to this mouza,nap_pr = Apartment plot proportion, rld_pr = Revenue proportion of land to this mouza, rap_pr = Apartment Revenue proportion;
d Tnap_pr = Apartment number contribution of this mouza to Total apartment Registration, Trap_pr = Apartment revenue contribution of this mouza to Total Registration.

In Fig 2.14 top 20 revenue contributing mouzas along with their proportionate share of revenue and that of numbers involved in transaction have been plotted.

From Table 2.12 & Fig 2.14 above it is found that:

• These 20 mouzas are mostly situated around Raniganj town, Andal area and Jamuria town. Also NH2 which runs across this jurisdiction has an important part in development of this area.
• 3 mouzas namely, Raniganj Municipality (14.76%), Searsole(7.77%) and Amrasata(6.21%) contributes most towards the revenue of ADSR Raniganj.
• All of these are ‘Municipality’ mouzas.
• Together they contribute about 12% of total number of transferred plots,
• Almost 50% of their revenue comes from apartment registration;
• Together they contribute almost 87% of the revenue coming from apartment registration;
• Apart from them, Bhadur of Andal PS is another mouza whose almost 50% of revenue comes from apartment registration;
• Ramprasadpur(2.38 dec), Raniganj Municipality(2.66 dec),Baska(3.24 dec),Andal(3.58 dec), Bhadur(3.66 dec), Dubchururia(3.70 dec),Seasole(4.26 dec),(excepting Raniganj Municipality and Searsole all fall in Andal PS) having low average area transfer.
• From Mouza-code it appaears all thesse Andal PS mouzas are more or less adjacent .It is supported by the Google imagery which says that they are situated in and around Andal Railstation and Andal ‘More’ (on NH-2) area which are well-marked urbanised area.²⁸
• Of these top mouzas, mouzas of Jamuria and Pandabeswar PS tend to transfer more area of land per transaction.

Of these mouzas Raniganj Municipality, Searsole and Amrasata have been taken up for further study of spatial distribution of market-value across the plot numbers and Fig 2.15 has been prepared. In it each dot represent a plot-number involved in transaction. Size of the dot varies with change in market-value - with size growing bigger for larger market-value. Plots involving land and apartment have also been coloured differently where transaction of land is represented by the blueish colour and that of apartment by the reddish one.

It is found that

• Registration generally tends to be concentrated in few pocekts.
• For Raniganj Municipality these pockets lay around the plot-range of
  • 1-250,400-600,800-900,around 1200;
  • sparsely around 1400,1600;
  • around 2500-2600,2700-2900;
  • sparsely around 5000-5100; -around 5700,6600 etc.
  • more higher market-values are seen near range 1-250, around 1900 and around 2900, which sometimes exceeded Rs 1Cr;
  • apartment registration concentrated around plot ranges 1-250,2700-2900,around 5100,and spasely around 3700-4200;
• For Searsole
  • concentration is witnessed around 1-100,around 400,around 1100-1200, around 2100-2600 and 4700-4900;
  • apartment registration is seen around 2100,around 2300,around 2500 and around 2800 and sparsely around 4800;
  • high values are seen near plots 100,700,4900;
• For Amrasata number of plots are less than the previous two mouzas;
  • concentration however is seen in the plot-range 1-300,around 500, sparsely in 1000-1700, around 2400,around 2900, more concentrated around 3000-3100.
  • apartment registration is seen around 300,1350,1400,2900,3000-3050;
  • high-valued transaction happened around 100,500,1200 and 3000.

2.3.2.2 Analysis of Proposed Land use practice:

Registration data of ADSR Raniganj has been analysed to get insight of the use of proposed land uses during registration. Also comparative analysis with adjecent offices of ADSR Asansol and ADSR Durgapur has been taken up.

Primarily we can start with the number of land groups present in these offices.(For detailed discussion please refer to 2.3.1.2). Table 2.13 shows the landgroups of the offices of Paschim Burdwan. Primarily, it is seen that ADSR Raniganj has much more larger landgroups(34) than other offices.

Table 2.13: Number of landgroups: Offices of Paschim Buradwan

District_code	Disrict	ro_code	office	num_of_landgrps
23	Paschim Bardhaman	04	A.D.S.R. RANIGANJ	34
23	Paschim Bardhaman	06	A.D.S.R. DURGAPUR	15
23	Paschim Bardhaman	24	A.D.S.R. KULTI	15
23	Paschim Bardhaman	05	A.D.S.R. ASANSOL	8

Then proposed landuses used for ADSR Raniganj and ADSR Asansol have been analysed. Fig 2.16 depict a comparative analysis of proposed landuse used in transactions for rural mouzas of ADSR Raniganj and ADSR Asansol. In it heights of the columns represent the proportion of use in transaction, each colour represent a different use and numbers besides the columns represent the respective conversion ratios.

Fig 2.16 shows that-

• Much more landuses were used in ADSR Raniganj compared to ADSR Asansol- as seen from the colour codes.
• Main landuses for both these offices revolve around different types of agrilands like Baid, residential like Bastu,Vastu etc, Industrial uses like Land for industrial use etc.
• Commercial types for both the offices have not been used much. It is expected as generally commercial transfers remain less in number compared to agri or residential purposes.
• ADSR Raniganj shows use of different identical or semi-identical landuses which can be easily clubbed and the number can be brought down as ADSR Asansol.
• large number of variation in the usages for ADSR Raniganj clearly shows that proposed land uses are relied heavily upon to reach the market-value of property.

Similar analysis has been undertaken for a municipality mouza each for ADSR Raniganj,ADSR Asansol and ADSR Durgapur. For ADSR Raniganj,mouza Raniganj Municipality; for ADSR Asansol, mouza Asansol Municipality; and for ADSR Durgapur, mouza Viringi have been chosen respectively. In Table2.14 a comparative analysis has been made by sorting out the conversion ratios used during land transfer of these mouzas. Also in it, number of uses belong to these ratios have been summarised. In Fig 2.17, these data has been plotted.

Table 2.14: Number of groups for Municipalities: Paschim Burdwan offices

Number of Conversion ratio Groups used
Mouza:Asansol Municiaplity	Mouza:Raniganj Municipality	Mouza:Virigi
ratio Number_of_uses 8 6 12 2 16 9 24 4 32 3	ratio Number_of_uses 2.5 2 4.0 1 4.5 1 6.0 1 8.0 1 10.0 4 12.0 3 13.0 2 20.0 2 35.0 4	ratio num 2.0 1 2.5 1 3.0 3 6.0 1 7.5 2 12.0 1

It is seen that

• In ADSR Raniganj much more number of ratios (groups) have been used;
• The number of ratios used for mouza Raniganj Municipality of ADSR Raniganj is 10;
• The number of ratios used for mouza Asansol Municipality of ADSR Asansol is 5;
• The number of ratios used for mouza Viringi of ADSR Raniganj is 6;
• Expectedly, for Raniganj Municipality much more number of groups exist. For example- 4 groups exist between ratios 4 to 8.
• Hence, for ADSR Raniganj market value determination is much more reliant on the use of conversion ratios- which may lead to more subjectivity.

In Table2.15 all the conversion ratios used for land transfers in these mouzas, their proportionate number, proportionate revenue coming from these ratios have been summarised.

Table 2.15: Comparative analysis of Proposed land uses used in Paschim Burdwan

Mouza:Asansol Municipality:Asansol	Mouza: Raniganj Municipality:Raniganj	Mouza: Viringi:Durgapur
use ratio prpnum prprv Bastu 16 55.78 54.08 Vastu 16 31.55 23.94 Other Commercial Usage 32 2.24 8.14 Semi Commercial Usages 24 2.06 7.39 Shop 32 1.40 1.73 Pukur 8 1.18 1.48 Path 16 3.33 0.87 Nursery 12 0.06 0.75 Dokan 32 0.37 0.62 Godown 24 0.28 0.42 Pukur Parh 16 0.65 0.20 Viti 16 0.16 0.12 Car Parking Space 16 0.03 0.08 Rasta 16 0.31 0.07 Baid 8 0.06 0.03 PUKUR 8 0.06 0.03 Danga 12 0.09 0.02 Karkhana/Factory 24 0.03 0.02 Doba 8 0.06 0.01 Bhiti 16 0.03 0.00 Drain 8 0.09 0.00 Gudam 24 0.06 0.00 Mandir 16 0.03 0.00 Nala/Dren 8 0.06 0.00	use ratio prpnum prprv Bastu 12.0 72.00 66.58 Vastu 12.0 12.16 11.76 Commercial Use 35.0 3.98 6.47 Other Commercial Usage 20.0 2.01 5.28 Pukur 4.0 4.43 2.82 Bagan 4.5 0.94 2.17 Danga 2.5 1.21 0.96 Khamar 6.0 0.72 0.80 Patit 10.0 0.49 0.74 Path 13.0 0.36 0.60 Dokan 35.0 0.13 0.47 Godown 20.0 0.45 0.44 Shop 35.0 0.09 0.33 Haspatal 10.0 0.04 0.30 Doba 8.0 0.09 0.13 School 10.0 0.04 0.11 Petrol Pump 35.0 0.09 0.01 Rasta 13.0 0.54 0.01 Drain 12.0 0.04 0.00 Mandir 10.0 0.04 0.00 Nala/Dren 2.5 0.13 0.00	use ratio prpnum prprv Bastu 3.0 47.67 51.81 Vastu 3.0 42.90 36.21 Other Commercial Usage 12.0 7.88 9.28 Factory/Karkhana 6.0 0.39 1.02 Semi Commercial 7.5 0.49 0.53 Land for Industrial use 2.5 0.19 0.52 Garej 7.5 0.10 0.30 Maszid 3.0 0.19 0.24 Pukur(Machhchash 2.0 0.19 0.08
1 use = Proposed Land Use name used during Transaction;
2 ratio= Respective conversion Ratio;
3 prpnum = Proportionate number of this particular use for this mouza’s land transaction;
4 prprv = Proportionate revenue coming from this use for this mouza

From Table2.15, it is seen that:

• for all these offices in municipality mouzas dominant proposed land uses are variants of ‘Residential uses’ like ‘Bastu’,‘Vastu’;
• Together these two contribute over 80% of total land transfers and about 75% of revenue from the respective mouza’s land transfers;
• Other dominant uses are ‘Commercial’,‘Semi commercial’ usages,together these two contribute about 10-15%.;²⁹
• For Raniganj Municipality mouza, uages like _‘Path(ratio=13)’,‘Patit( ratio= 10)’_have been found to be used.
• These usgaes are around ‘Bastu’/‘Vaastu’(ratio=12), and can easily be clubbed with ‘Bastu’.
• ’Patit’seems to have been used for less-developed plots near residential areas.
• Rather than this, the concerned plots can be identified properly and their respective basevalues can be updated accordingly;

2.3.2.3 Assessment of Registration pattern:

Registration data of select offices can be taken up to have insights over the distribution of different transaction types, seasonal distribution of transactions, distribution of registration types.

2.3.2.4 Combining the Registration data with othet available data :

Registration data as discussed above can be analysed interacively with different sorts of data available on different platforms. These data can be ranged from available GIS data to ROR data, census data, road information if available from PWD, GST data with vendor information , data from agriculture department showing cropping intensity etc. These can render insights to know the nature of an area and to assess logically its potential as residential, agricultural, commercial or industrial area.

I.GIS data-

DoRSR has set up its own GIS project.

• In it Shape files, containing the basic plot information-such as plot-number,area of all mouzas as available from L& LR department, Govt. of West Bengal, are being georeferenced at HQ level³².
• Also shape files of metal rd,SH, NH as on 2014, have been made available to DoRSR and those have been put in one separate layer alon with the georefernced plot level shape files.
• The registration data as discussed earlier, ROR information data have been put into the attributes of the GIS data received and made ready for on-the-click view (See Figure 2.22).
• Also using GIS tools, areal distances of all plots from selected fetatures³³ can be calculated.
• Some basic analytics features are available in the GIS platform which can be helpful in visualising the output over maps;
• Also all the data put into GIS attributes can be exported in excel or csv format which can be taken up for further analysis;
• Also R or Python scripts used in these analysis can be put in the GIS directly for visualising the outputs over maps.

II. ROR data

ROR data acquired from L&LR department can be analysed simultaneously with registration data. Even GIS can render useful information like distance from selected features like ‘Bastu’ plot,‘Metal Road’ etc. Land classes can then be grouped and summarised over these distances to get the picture of general location of these classes relative to these features. Table 2.17 is an example of such grouping. It lists 20 classes- sorted alphabetically, under the jurisdiction of ADSR Durgapur.

Table 2.17: Different land classes under ADSR Durgapur and distances from Bastu, Metal_Rd:Example

Land Class	Number	Mid_Bastu	Mid_Mtl_Rd
Bagaan	524	54	174
Baid	56817	255	385
Bakery	1	37	722
Baluchar	137	59	165
Bandh	45	197	149
Bans Bagaan	3	0	273
Bastu	15503	0	44
Bazaar	24	8	10
Beel	1	30	329
Bhagar	1	71	6
Bhiti	1229	0	58
Bohal	2441	238	531
Chanak Khad	2	124	174
Chankhola	10	0	398
Chaul Kal	1	87	81
Club	4	6	137
College	4	0	64
Dairy	1	45	12
Danga	6825	22	104
Debsthan	355	0	56
1 Land Class= Land Class as in ROR
2 Number = Respective number of each land class
3 Mid_Bastu = Median distance from nearest Bastu plots
4 Mid_Mtl_Rd = Median distance from neasest Metal Road

As these classes are put into groups and thereby conversion ratios by ADSRs - this analysis can be helpful be for preparation of proposal on conversion ratios by the ADSRs.

Fig 2.23 has been prepared by plotting the different land classes which are located within 100 mtrs (median distance) of ‘Bastu’ class.Each class is shown by a separate point and their sizes vary with the change in their respective number of occurrence in ROR data.

III.Census Data

Census data can be analysed with respect to the population of a particular area and can be explored whether number of transactions or market-value of immovable properties have any bearing with it. To check primarily, census data (2011) of areas under ADSR Raniganj have been taken up and in Fig 2.24 most populous areas with their respective population have been plotted.³⁴

It is found that roughly, 60% of the places picked in Fig 2.24 coincide with the mouzas with most registrations under the jurisdiction of ADSR Raniganj. (Please refer to Fig 2.14). Hence there is ample scope of exploring this data along side,in analysis of market-value for ‘Scientific Framework’.

Similarly, GST data with details of dealers, the data from Agriculture department showing the cropping intensity, the data from PWD deapartment with road-details can be helpful in this regard.

Objective would be to explore wheather there are any relationship with market value and with them;

2.3.2.5 Building MV Model:

Generally for market- value determination of a property or preparation of MVDB ROs keep in mind a number of implicit or explicit factors which may be responsible for the market value of property. Although, ROs employ their field experience and application of mind- even the best practices may be very subjective. However, these factors or variables are very much quantifiable . They can be either continuous - i.e, measurable on any continuous scale like distance; or, categorical - i.e few qualities that can be grouped or ordered into some measurable quantities; or, binary - i.e, when presence of absence of any particular quality can be described by 1 or 0. Then collecting the data of market-value of property in a mixed way -

• by independent means from field with the help of survey;
• and by picking up the registration data where data-quality is better;

and using the factors responsible for market-value, with the help of statistical analytics tools, ’Market Value Model’s can be built.

However, if the chosen parameters can be reconciled with the outcomes of fileld survey and a diverse and vast amount of market-value data is collected from field the model can be more reflective of ground reality. Hence, coming on board of Indian Staistical Institute - Kolkata,as decided earlier, for conducting representative sample survey and building such model/models for representative areas, as well as training the DoRSR officers to conduct such for themselves in future would be very sought after.

Nevertheless, an attempt has been made to build such a model for a few select areas of ADSR Durgapur in the following way: (Please see Fig 2.26 which outlines the process.)

• First a part of ADSR registration data has been chosen;
• Distance from nearest bastu plot, nearest metal road have been measured for each plot;
• Then each mouza has been categorised as per their yearly registration numbers (See Fig 2.25);
• Then the data was parted into two parts- namely, trainset and testset;
• For trainset regression has been done with known market-value of this set and set of parametrs to build the model;
• Regression has been done to reach at ‘Market Value’ with different sets of parameters

  • One set with parameters -

    • Distance from Bastu,
    • Distance from metal road,
    • area of land,,
    • ROR classification,
    • Mouza-type and
    • Plots-transfer range;

  • another set with parameters-

    • Distance from Bastu,
    • Distance from metal road,
    • area of land,,
    • Mouza-type and
    • Plots-transfer range

  • For both these sets multiple regression methods were followed.³⁵
    

  • It is found that the model with second set of parameters stated above yielded the best Rsquared³⁶ value i.e without ROR classification³⁷.

  • Then this model was employed to predict the market-value of the testset.

  • It has been found that parameters area of land,distance from metal road,mouza-type(Municipality1), plots-transfer-range(above 1K), distance from bastu , mouza_type(Rural) - ordered in descending order of importances- play most important determination of market-value in the best model.

  • Nevertheless, it should be kept in mind that, as these models have been built by training the existing the market value of the train-set which is the market-value generated by the ‘e-nathikaran’ system, the underlying weaknesses inherent in the system might creep in these models. Hence the market-value data of the train-set should be a mixture of both the data from field survey, as well as good market value data, which can be a near reflection of proper market-value on ground, identified from the system.

Hence analytics can play a very crucial role for DoRSR. It can be employed effectively for regular analysis of revenue data to visualize the trends in different zones of the state, to unearth the underlying pattern, similarities or dissimilarities at the revenue front across offices beyond district-limits and to lay stress on the typically untouched segments for revenue generation as well as for informed decision making- as it will help DoRSR looking beyond the numbers of revenue data only.

As analytics can be used for much deeper analysis of the office wise registration data, it can be helpful in detection of anomaly in mouza groups, conversion ratios, base values in effective way. It can render quality service to the DoRSR by helping the ROs in identifying the potential growth centers, by making them aware of the pattern of registration, spatial and seasonal distribution of market-value of properties across different mouzas or across different plot-numbers of selected mouzas under their respective jurisdictions. It can even be proved useful in rendering insights on the registration practices prevalent in an office, about the time-window of the congregation of registrant public at office and thereby on the possible pattern of its public-dealing.

Analytics can be helpful in exploring a range of GIS information, various data available on different government or other platforms in respect of market value of properties and can be effectively utilized in building market value models by making use of these data and any data that might come out of field survey. Hence it is recommended to start working of a full-fledged regular analytics wing at the headquarter level of DoRSR.